{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a92ba4c3",
   "metadata": {},
   "source": [
    "# Data Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f316ba02",
   "metadata": {},
   "source": [
    "The data generator module supports generating both time series and tabular causal graphs, and data of continuous and discrete types. \n",
    "\n",
    "### Graph Generation\n",
    "\n",
    "For both, tabular and time series data, we support two methods for generating synthetic causal graphs.\n",
    "\n",
    "**Tabular**: GenerateRandomTabularSEM, GenerateSparseTabularSEM\n",
    "\n",
    "**Time Series**: GenerateRandomTimeseriesSEM, GenerateSparseTimeSeriesSEM\n",
    "\n",
    "The main difference between GenerateRandomSEM and GenerateSparseSEM is that the former requires the maximum number of parents when generating a random SEM, while the latter requires the graph density.\n",
    "\n",
    "Their descriptions are as follows:\n",
    "\n",
    "- **GenerateRandomTabularSEM**: Generate a random structural equation model (SEM) for tabular data. It takes the following arguments:\n",
    "    1. var_names (List of str): Names of variables in the SEM in the form of a list of str.\n",
    "    2. max_num_parents (int): Maximum number of causal parents allowed in the randomly generated SEM. (default: 4)\n",
    "    3. seed (int): Random seed used for reproducibility.\n",
    "    4. fn (Callable): Function applied to a parent variable when generating child variable data. Default: Linear \n",
    "        function for linear causal relation.\n",
    "\n",
    "\n",
    "- **GenerateSparseTabularSEM**: Generate a structural equation model (SEM) for tabular data using the following procedure:\n",
    "    For N nodes, enumerate them from 0-N. For all i,j between 0-N, if i < j, the edge from vi \n",
    "    to vj exists with probability graph_density, and if i >= j there cannot be an edge betwen them. It takes the following arguments:\n",
    "    1. var_names (List of str): Names of variables in the SEM in the form of a list of str.\n",
    "    2. graph_density (float): Probability that an edge between node i and j exists.\n",
    "    3. seed (int): Random seed used for reproducibility.\n",
    "    4. fn (Callable): Function applied to a parent variable when generating child variable data. Default: Linear \n",
    "        function for linear causal relation.\n",
    "    5. coef (float): coefficient of parent variables in the randomly generated SEM. Note: larger values may \n",
    "        cause exploding values in data array for some seeds.\n",
    "\n",
    "\n",
    "\n",
    "- **GenerateRandomTimeseriesSEM**: Generate a random structural equation model (SEM) for time series data using the following procedure:\n",
    "    Randomly divide variables into non-overlapping groups of size between 3 and num_vars. Then randomly\n",
    "    create edges between a preceeding group and a following group such that max_num_parents is never exceeded. It takes the following arguments:\n",
    "    1. var_names (List of str): Names of variables in the SEM in the form of a list of str.\n",
    "    2. max_num_parents (int): Maximum number of causal parents allowed in the randomly generated SEM. (default: 4)\n",
    "    2. max_lag (int): Maximum time lag between parent and child variable allowed in the randomly generated SEM. (default: 4)\n",
    "    3. seed (int): Random seed used for reproducibility.\n",
    "    4. fn (Callable): Function applied to a parent variable when generating child variable data. Default: Linear \n",
    "        function for linear causal relation.\n",
    "\n",
    "\n",
    "- **GenerateSparseTimeSeriesSEM**: Generate a structural equation model (SEM) for time series data using the following procedure:\n",
    "    For N nodes, enumerate them from 0-N. For each time lag (until max_lag), for all i,j between 0-N, if i < j, \n",
    "    the edge from vi to vj exists with probability graph_density, and if i >= j there cannot be an edge \n",
    "    betwen them. It takes the following arguments:\n",
    "    1. var_names (List of str): Names of variables in the SEM in the form of a list of str.\n",
    "    2. graph_density (float): Probability that an edge between node i and j exists.\n",
    "    3. max_lag (int): Maximum time lag between parent and child variable allowed in the randomly generated SEM. Must be non-negative.\n",
    "    4. seed (int): Random seed used for reproducibility.\n",
    "    5. fn (Callable): Function applied to a parent variable when generating child variable data. Default: Linear \n",
    "        function for linear causal relation.\n",
    "    6. coef (float): coefficient of parent variables in the randomly generated SEM. Note: larger values may \n",
    "        cause exploding values in data array for some seeds.\n",
    "        \n",
    "\n",
    "### Data Generation\n",
    "\n",
    "The function **DataGenerator** is used for generating data samples for a given structural equation model (SEM). The function has several arguments:\n",
    "\n",
    "1. sem (Python dictionary): specifies the causal graph in the form of aa Python Dctionary and implicitly contains the information whether the data is time series or tabular data. \n",
    "2. T (int): specifies the number of sample or length of time series, depending on whether the data i tabular or time series.\n",
    "3. noise_fn (List of Callable): List of functions each of which takes t as input and that returns a random vector of length t. The ith index corresponds to the ith key in sem. (default: list of np.random.randn)\n",
    "4. intervention (Python dictionary): Dictionary of format: {1:np.array, ...} containing only keys of intervened variables with the value being the array of length T with interventional values. Set values to np.nan to leave specific time points of a variable un-intervened.\n",
    "5. discrete (bool or Python dictionary): When bool, it specifies whether all the variables are discrete or all of them are continuous. If true, the generated data is discretized into nstates uniform bins (default: False). Alternatively, if discrete is specified as a dictionary, then the keys of this dictionary must be the variable names and the value corresponding to each key must be True or False. A value of False implies the variable is continuous, and discrete otherwise. (default: False)\n",
    "6. nstates (int): When discrete is True, the nstates specifies the number of bins to use for discretizing the data (default=10). Ignored for continuous data.\n",
    "7. seed (int): Set the seed value for random number generation for reproduciblity (default: 1).\n",
    "\n",
    "As an example, for tabular data, the following is a sample SEM,\n",
    "$$A = \\mathcal{N}_A()$$\n",
    "$$B = k_1 \\times F_1(A) + \\mathcal{N}_B()$$\n",
    "$$C = k_2 \\times F_2(A) + k_3 \\times F_3(B) + \\mathcal{N}_C()$$\n",
    "\n",
    "where $\\mathcal{N}_i$'s are functions that return random samples from some distribution (E.g. Gaussain), $k_i$'s are constants, and $F_i$'s are callable functions that transforms the argument variable value. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0e56d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle as pkl\n",
    "from causalai.misc.misc import plot_graph\n",
    "from causalai.data.data_generator import DataGenerator, GenerateRandomTabularSEM, GenerateRandomTimeseriesSEM\n",
    "from causalai.data.data_generator import GenerateSparseTimeSeriesSEM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696845db",
   "metadata": {},
   "source": [
    "## Graph Generation\n",
    "\n",
    "Here we show an example of generating a graph for tabular and time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b256fc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': [('c', 0.1, <function __main__.<lambda>(x)>),\n",
       "  ('f', 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'b': [('f', 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'c': [('b', 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'd': [('f', 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'e': [('b', 0.1, <function __main__.<lambda>(x)>),\n",
       "  ('f', 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'f': []}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem_tabular = GenerateRandomTabularSEM(var_names=['a', 'b', 'c', 'd', 'e', 'f'],\n",
    "                                       max_num_parents=4, seed=0, fn = lambda x:x)\n",
    "sem_tabular"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b77ec1",
   "metadata": {},
   "source": [
    "SEM is provided as a Python dictionary with variable names as keys, and the list of children as values. Specifically, each child is of the format (parent, coef, function), as explained in the example above under Data Generation in the equations for an SEM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2710e6bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': [],\n",
       " 'b': [],\n",
       " 'c': [(('b', -1), 0.1, <function __main__.<lambda>(x)>),\n",
       "  (('a', -2), 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'd': [],\n",
       " 'e': [(('c', -2), 0.1, <function __main__.<lambda>(x)>)],\n",
       " 'f': [(('c', -3), 0.1, <function __main__.<lambda>(x)>)]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem_time_series = GenerateSparseTimeSeriesSEM(var_names=['a', 'b', 'c', 'd', 'e', 'f'],\n",
    "                                       graph_density=0.1, max_lag=4, seed=0, fn = lambda x:x, coef = 0.1)\n",
    "\n",
    "sem_time_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253f5b4a",
   "metadata": {},
   "source": [
    "SEM is provided as a Python dictionary with variable names as keys, and the list of children as values. Specifically, each child is of the format ((parent, time_lag), coef, function), as explained in the example above under Data Generation in the equations for an SEM. Note that time_lag is specified as -k (e.g. -1) which is to be read as t-1, where t is the current time step of the parent. So for node 'c', one of the parents is ('b',-1), which implies b[t-1], which is a parent of c[t]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ac73fe",
   "metadata": {},
   "source": [
    "## Data Generation without intervention\n",
    "\n",
    "Let's start with the simple case of data generation without any interventions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b063ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = lambda x:x\n",
    "F1=F2=F3 = fn\n",
    "k = 0.1\n",
    "k1=k2=k3=k\n",
    "noise_fn = np.random.randn\n",
    "noise_fn_a= noise_fn_b= noise_fn_c = noise_fn\n",
    "sem_tabular = {\n",
    "        'a': [], \n",
    "        'b': [('a', k1, F1)], \n",
    "        'c': [('a', k2, F2), ('b', k3, F3)],\n",
    "        }\n",
    "\n",
    "sem_time_series = {\n",
    "        'a': [], \n",
    "        'b': [(('a', -1), k1, F1)], \n",
    "        'c': [(('a', -2), k2, F2), (('b', -2), k3, F3)],\n",
    "        }\n",
    "\n",
    "\n",
    "T = 5000 # number of samples in case of tabular data, and length of time series in case of time series data\n",
    "data_tabular, var_names_tabular, graph_gt_tabular = DataGenerator(sem_tabular, T=T, seed=0, discrete=False,\\\n",
    "                                            noise_fn=[noise_fn_a, noise_fn_b, noise_fn_c])\n",
    "\n",
    "data_time_series, var_names_time_series, graph_gt_time_series = DataGenerator(sem_time_series, T=T,\\\n",
    "                                                            seed=0, discrete=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "feab6f49",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WhtmzZ+Pnn3+GTqfD/PnzUVNTIzoaERER1QEWSgv5+/sjLCzM4d/tbSk3NzeMHz8eBoMBCQkJGDt2LLp164adO3eKjkZEREQ2xkJpBbIsY+XKlaisrBQdRbgWLVrg66+/RmpqKgCgZ8+eePLJJ3H27FnByYiIiMhWWCitQK/X4/Lly9iwYYPoKHajZ8+e2L17N+bNm4ekpCRotVp8/vnnMJlMoqMRERGRlbFQWkFQUBC0Wq3Lj73/TK1WY+zYsTAYDLjvvvvw/PPPIzIyEr/99pvoaERERGRFLJRWIEkS9Ho9li9fzhW4v9CsWTN89dVX2LlzJ9zd3dGrVy88+uijOHPmjOhoREREZAUslFYiyzJKSkqwdetW0VHs1tVNOl999RVWrVoFrVaLzz77jCWciIjIwbFQWklERATatWvncoec3yq1Wo0xY8YgPz8fDz30EF588UWEh4eziBMRETkwFkorkSQJsiwjKSmJryG8CT4+Pvjiiy+wZ88eeHt7o3fv3nj44Ydx+vRp0dGIiIjoFrFQWpEsyzh9+jTPXrwFERERSE1NxcKFC7F27VrodDrMmDED1dXVoqMRERHRTWKhtKKoqCj4+vpyt/ctUqlUeOKJJ2AwGDBq1ChMnDgRnTt3xq+//io6GhEREd0EFkorUqvVGDp0KJYuXQpFUUTHcThNmjTB559/jr1796Jx48bo168fHnjgAZw8eVJ0NCIiIroBFkor0+v1OHLkCPbt2yc6isPq3Lkztm/fju+++w6//vordDodpk2bhqqqKtHRiIiI6C+wUFpZnz590KRJE+72tpAkSRg1ahTy8/MxevRovP766wgLC+PbiIiIiOwQC6WVubu7Y8iQIXyO0koaN26MWbNmIT09Hc2bN8eAAQNw33334fjx46KjERER0f+wUNqALMvIzc1Fbm6u6ChOIzQ0FFu2bMEPP/yA7du3IyAgAB9++CEqKytFRyMiInJ5LJQ2cM8996BevXpcpbQySZIwcuRI5Ofn45lnnsGbb76JTp06Ye3ataKjERERuTQWShvQaDS49957+RyljTRs2BDTp09HZmYmWrdujdjYWAwbNgxHjhwRHY2IiMglsVDaiF6vR0ZGBg4fPiw6itMKDg7Gpk2bsHjxYuzevRuBgYF47733UFFRIToaERGRS2GhtJFBgwbB09OTY28bkyQJDzzwAPLy8vDcc8/h3XffRUhICFatWiU6GhERkctgobSRBg0aYODAgSyUdaRBgwaYNm0a9u/fj/bt2yMuLg5DhgzBoUOHREcjIiJyeiyUNiTLMlJTU3Hq1CnRUVxGQEAA1q1bh//+97/Yt28fgoKC8M4778BoNIqORkRE5LRYKG0oPj4ebm5uWLZsmegoLkWSJAwfPhy5ubl46aWXMHXqVAQHB2PFihV8JSYREZENsFDaUNOmTdG3b1+OvQWpV68epk6diuzsbGi1WiQkJODee+/FwYMHRUcjIiJyKiyUNqbX67F582aUlJSIjuKytFotUlJSkJiYiJycHAQHB+ONN95AeXm56GhEREROgYXSxhISEmA2m7FixQrRUVyaJEkYNmwYcnNz8eqrr+KTTz5BYGAgEhMTOQYnIiKyEAuljfn5+aFXr1485NxOeHt747333kNOTg5CQkKg1+sRGxsLg8EgOhoREZHDYqGsA7IsY/369bh06ZLoKPQ/d911F1auXIkVK1agoKAAISEh+Ne//oWysjLR0YiIiBwOC2UdkGUZVVVVPGzbzkiShPj4eOTk5GDy5MmYNWsWAgIC8N///pdjcCIiolvAQlkH2rVrh8jISO72tlNeXl54++23ceDAAYSHh2PEiBEYMGAAcnNzRUcjIiJyCCyUdUSv12P16tXcWWzH2rdvj+XLl2PVqlU4cuQIQkNDMXHiRFy+fFl0NCIiIrvGQllHZFlGeXk51q1bJzoK/YPBgwcjOzsbb7/9NubOnYuAgAAsXryYY3AiIqK/wUJZR7RaLYKDg7nb20FoNBq88cYbyM3NRffu3fHQQw+hX79+yMnJER2NiIjI7rBQ1iG9Xo/k5GRUVVWJjkI36Y477kBiYiLWrFmDU6dOISwsDC+99BJ37BMREf0BC2UdkmUZFy9exKZNm0RHoVs0cOBAZGVl4f3338eXX34JnU6HH374gWNwIiIisFDWqdDQUPj7+3O3t4Py9PTEpEmTkJeXh+joaDzyyCPo3bs3srKyREcjIiISioWyDkmSBFmWsWzZMtTU1IiOQ7epbdu2+Pnnn7F+/XqcPXsW4eHhmDBhAkpLS0VHIyIiEoKFso7p9XqcPXsW27dvFx2FLBQTE4PMzEx8+OGHWLhwIXQ6Hb777juYzWbR0YiIiOoUC2Ud69q1K1q3bs3d3k7Cw8MDEydORH5+Pvr164fHHnsM0dHRyMjIEB2NiIiozrBQ1jGVSgVZlpGYmMiVLCfSunVrLF68GJs2bUJpaSkiIyMxbtw4XLhwQXQ0IiIim2OhFECWZZw8eRJ79uwRHYWsrG/fvti3bx8+/fRTfP/999BqtVi4cCG/eSAiIqfGQilAdHQ0mjdvzt3eTsrd3R0vvvgi8vPzERsbi9GjRyMqKgppaWmioxEREdkEC6UAarUaCQkJSExM5DmGTqxly5b4/vvvsXXrVpSXl6Nbt2545plnUFJSIjoaERGRVbFQCqLX63Hw4EHs379fdBSysejoaKSnp2PWrFlYvHgxtFotvvrqKx4dRUREToOFUpB+/fqhUaNGHHu7CDc3Nzz//PMwGAyIj4/H008/jR49emD37t2ioxEREVmMhVIQDw8PxMfH8/ggF+Pr64tvv/0Wv/32G0wmE3r06IExY8bg7NmzoqMRERHdNhZKgWRZRnZ2NgwGg+goVMeubtL5/PPP8csvv0Cn02HevHkcgxMRkUNioRRo4MCB8Pb25tjbRanVajz77LMwGAyQZRnjxo1D165dsWPHDtHRiIiIbgkLpUDe3t4YNGgQC6WLa968Of79739j586dUKlUiIqKwuOPP47i4mLR0YiIiG4KC6Vger0ee/bswbFjx0RHIcG6d++OXbt2Yf78+VixYgW0Wi3mzJkDk8kkOhoREdENsVAKdu+998LDwwNJSUmio5AdUKvVePrpp2EwGHD//fdjwoQJiIiIwPbt20VHIyIi+lsslII1bNgQAwYM4G5vqsXHxwdffvkldu3aBY1Gg+joaIwaNQpFRUWioxEREV2HhdIOyLKM7du348yZM6KjkJ25uklnwYIFWL16NXQ6HWbNmoXq6mrR0YiIiK5hobQDQ4YMgUqlwrJly0RHITukUqkwevRoGAwGPPzww3jppZcQHh6OLVu2iI5GREQEgIXSLjRr1gy9e/fmbm+6oaZNm2Lu3LlIS0tDgwYN0KdPHzz00EM4deqU6GhEROTiWCjthCzL2LRpEy5cuCA6Ctm58PBwbN++Hd988w02bNgAnU6HTz/9lGNwIiIShoXSTgwbNgwmkwnJycmio5ADUKlUeOyxx2AwGPD444/jtddeQ1hYGDZt2iQ6GhERuSAWSjvRqlUr9OzZk7u96ZY0btwYs2fPRnp6Onx8fNC/f3/cf//9OHHihOhoRETkQlgo7Yher8fatWtx5coV0VHIwYSFhWHr1q1YtGgRtmzZgoCAAHz88ceoqqoSHY2IiFwAC6UdkWUZlZWVSElJER2FHJAkSXjkkUeQn5+PMWPGYPLkyejUqRPWrVsnOhoRETk5Fko70r59e3Tp0oVjb7JIo0aNMHPmTOzbtw9+fn4YOHAg9Ho9X+9JREQ2w0JpZ2RZxqpVq1BRUSE6Cjm4kJAQbN68GT/99BN27NiBgIAAfPDBB6isrBQdjYiInAwLpZ3R6/W4cuUK1q9fLzoKOQFJkvDggw8iPz8f48aNwzvvvIOQkBA+VkFERFbFQmlnAgMDERAQwEPOyaoaNGiATz75BJmZmWjXrh0GDx6MoUOH4siRI6KjERGRE2ChtEN6vR7Lly/nQdVkdUFBQdiwYQOWLFmCtLQ0BAYG4t133+UjFkREZBEWSjskyzIuXLjAdzWTTUiShBEjRiAvLw8TJkzA+++/j+DgYKxcuVJ0NCIiclAslHaoS5cuuPPOO7nbm2yqfv36+Oijj7B//374+/sjPj4e8fHxKCwsFB2NiIgcDAulHZIkCbIsIykpCTU1NaLjkJPT6XRYu3Ytli5diszMTAQHB+Ott95CeXm56GhEROQgWCjtlF6vx5kzZ7Bjxw7RUcgFXP0mJjc3F6+88go+/vhjBAUFYdmyZVAURXQ8IiKycyyUdqpHjx5o2bIld3tTnapXrx7ef/99ZGdnIygoCMOGDcPgwYNRUFAgOhoREdkxFko7pVKpMGzYMCQmJnKFiOpcx44dsWrVKixbtgx5eXkICQnB5MmTUVZWJjoaERHZIRZKOybLMo4ePYr09HTRUcgFSZKEhIQEHDhwAJMmTcL06dMRGBiIpUuX8pscIiKqhYXSjvXu3RtNmzblbm8SysvLC1OmTEFOTg7CwsIwfPhwDBw4EPn5+aKjERGRnWChtGNubm5ISEjgihDZBX9/fyQnJyM5ORmFhYXo1KkTJk2ahCtXroiORkREgrFQ2jm9Xg+DwYADBw6IjkIEAIiLi0NOTg7eeOMNfPbZZwgICMCSJUv4TQ8RkQtjobRzMTExaNCgAXd7k13RaDR46623cODAAXTt2hUPPPAAYmJi+I0PEZGLYqG0c56enoiLi+NzlGSX2rdvj6SkJKSkpODYsWMICwvDK6+8gsuXL4uORkREdYiF0gHIsozMzEy+Eo/sVmxsLLKzszFlyhTMmzcPOp0OP/30E8fgREQugoXSAcTGxkKj0SApKUl0FKK/5enpiddffx15eXmIiorCyJEj0bdvX2RnZ4uORkRENsZC6QDq16+P2NhYjr3JIbRr1w6//PIL1q1bh6KiInTu3BkvvvgiLl68KDoaERHZCAulg5BlGTt37sTJkydFRyG6KQMGDEBWVhY++OADLFiwADqdDt9//z3H4EREToiF0kHEx8fDzc2NY29yKB4eHnjttdeQl5eH3r17Y9SoUYiOjkZmZqboaEREZEUslA6icePG6N+/P48PIofUpk0bLFmyBBs2bMD58+cRHh6O5557DqWlpaKjERGRFbBQOhC9Xo8tW7bg7NmzoqMQ3Zb+/fsjMzMT06ZNw7fffgutVotvvvkGZrNZdDQiIrIAC6UDSUhIAACsWLFCcBKi2+fu7o6XX34Z+fn5GDBgAJ544gncfffdSE9PFx2NiIhuEwulA2nRogWio6O525ucQqtWrfDjjz9i8+bNuHLlCiIjI/Hss8/i/PnzoqMREdEtYqF0MLIsY8OGDTyChZxG7969kZ6ejhkzZuDHH3+EVqvFggULOAYnInIgLJQOZtiwYaiursbKlStFRyGyGnd3d7zwwgvIz8/H4MGD8dRTT6FHjx7Ys2eP6GhERHQTWCgdTNu2bdGtWzfu9ian5Ofnh0WLFmHbtm2oqqpC9+7d8dRTT+HcuXOioxER0Q2wUDogvV6PlJQUlJWViY5CZBO9evVCWloaZs+ejZ9//hk6nQ7z589HTU2N6GhERPQXWCgdkCzLMBqNWLt2regoRDbj5uaG8ePHw2AwICEhAWPHjkX37t2xc+dO0dGIiOhPWCgd0F133YXQ0FDu9iaX0KJFC3z99ddITU2F2WxGz5498eSTT/I8ViIiO8JC6aBkWcbKlStRWVkpOgpRnejZsyf27NmDefPmISkpCVqtFnPnzuUYnIjIDrBQOii9Xo9Lly5h48aNoqMQ1Rm1Wo2xY8fCYDBg+PDheO655xAZGYnffvtNdDQiIpfGQumggoOD0bFjR+72JpfUrFkzLFiwADt37oSbmxt69eqFRx99FGfOnBEdjYjIJbFQOihJkqDX67Fs2TKYTCbRcYiE6NatG3bu3ImvvvoKq1atglarxWeffcY/E0REdYyF0oHJsoySkhJs27ZNdBQiYdRqNcaMGYP8/Hw89NBDePHFFxEeHo6tW7eKjkZE5DJYKB1YZGQk2rZty7E3EQAfHx988cUX2LNnD7y9vdG7d288/PDDOH36tOhoREROj4XSgUmSBFmWkZiYyPceE/1PREQEUlNTsXDhQqxduxY6nQ4zZsxAdXW16GhERE6LhdLB6fV6nDp1Crt37xYdhchuqFQqPPHEEzAYDBg1ahQmTpyILl26YPPmzaKjERE5JRZKBxcVFYUWLVrwkHOiv9CkSRN8/vnn2Lt3Lxo1aoS+ffviwQcfxMmTJ0VHIyJyKiyUDk6tVmPYsGFITEyEoiii4xDZpc6dO2Pbtm349ttvsWnTJuh0OkybNg1VVVWioxEROQUWSicgyzIOHTqEzMxM0VGI7JZKpcKjjz6K/Px8jB49Gq+//jrCwsKwYcMG0dGIiBweC6UT6NOnDxo3bszd3kQ3oXHjxpg1axbS09PRvHlzDBgwAPfddx+OHz8uOhoRkcNioXQCHh4eGDJkCJ+jJLoFoaGh2LJlC3744Qds374dAQEB+PDDD1FZWSk6GhGRw2GhdBKyLOPAgQPIy8sTHYXIYUiShJEjRyI/Px/PPPMM3nzzTXTq1Alr164VHY2IyKGwUDqJe+65B/Xq1ePYm+g2NGzYENOnT0dmZiZat26N2NhYyLKMo0ePio5GROQQWCidhJeXFwYPHsxCSWSB4OBgbNq0CYsXL8auXbsQGBiI999/HxUVFaKjERHZNRZKJ6LX67F3714cOXJEdBQihyVJEh544AHk5eVh/PjxmDJlCkJCQrBq1SrR0YiI7BYLpRMZPHgwPD09kZSUJDoKkcNr0KABpk2bhv3796N9+/aIi4vDkCFDcOjQIdHRiIjsDgulE2nQoAHuuece7vYmsqKAgACsW7cO//3vf5GRkYGgoCC88847MBqNoqMREdkNFkonI8syUlNTcfr0adFRiJyGJEkYPnw48vLy8NJLL2Hq1KkIDg7GihUr+IYqIiKwUDqdIUOGQKVSYdmyZaKjEDmdevXqYerUqcjOzoZWq0VCQgLi4uJw8OBB0dGIiIRioXQyTZs2Rd++fbnbm8iGtFotUlJSkJiYiOzsbAQHB+PNN99EeXm56GhEREKwUDohvV6PX3/9FSUlJaKjEDktSZIwbNgw5Obm4tVXX8W0adMQFBSEpKQkjsGJyOWwUDqhoUOHwmw2Izk5WXQUIqfn7e2N9957Dzk5OQgODoYsy4iNjYXBYBAdjYiozrBQOiE/Pz/cfffdHHsT1aG77roLK1euxIoVK1BQUICQkBD861//QllZmehoREQ2x0LppGRZxrp163D58mXRUYhchiRJiI+PR05ODiZPnoxZs2YhICAA//3vfzkGJyKnxkLppGRZRmVlJVavXi06CpHL8fLywttvv42cnByEh4djxIgRuOeee5Cbmys6GhGRTbBQOqk77rgDERERPOScSKAOHTpg+fLlWLVqFQ4fPozQ0FC8+uqrnBwQkdNhoXRier0eq1ev5hs9iAQbPHgwsrOz8fbbb+Pzzz9HQEAA/vOf/3AMTkROg4XSicmyjLKyMqxbt050FCKXp9Fo8MYbbyA3Nxfdu3fHgw8+iP79+yMnJ0d0NCIii7FQOjGdTofg4GDu9iayI3fccQcSExOxZs0anDhxAmFhYXj55Zdx6dIl0dGIiG4bC6WTk2UZK1asQFVVlegoRPQHAwcOxP79+/H+++9j/vz50Ol0+OGHHzgGJyKHxELp5PR6PUpLS7F582bRUYjoTzw9PTFp0iTk5eUhOjoajzzyCHr37o2srCzR0YiIbgkLpZMLDQ1Fhw4duNubyI61bdsWP//8M9avX4+zZ88iPDwcEyZMQGlpqehoREQ3hYXSyUmSBFmWsWzZMtTU1IiOQ0Q3EBMTg8zMTHz44YdYuHAhdDodvvvuO5jNZtHRiIhuiIXSBej1ehQXF+O3334THYWI/oGHhwcmTpyI/Px89OvXD4899hiio6Oxb98+0dGIiP4WC6UL6NatG1q1asXd3kQOpHXr1li8eDE2bdqE0tJSREREYPz48bhw4YLoaERE12GhdAEqlQqyLCMxMZE7SIkcTN++fbFv3z588sknWLRoEbRaLb7++muOwYnIrrBQughZlnH8+HGkpaWJjkJEt8jd3R0vvfQS8vPzERsbiyeffBJRUVHYu3ev6GhERABYKF1GdHQ0mjVrxt3eRA6sZcuW+P7777F161aUl5eja9eueOaZZ1BSUiI6GhG5OBZKF+Hm5oaEhAQsXbqUY28iBxcdHY309HTMmjULixcvhlarxVdffcWTHIhIGBZKF6LX63Hw4EFkZ2eLjkJEFnJzc8Pzzz8Pg8GA+Ph4PP300+jRowd2794tOhoRuSAWShfSr18/NGzYkLu9iZyIr68vvv32W/z2228wmUzo0aMHxowZg3PnzomORkQuhIXShXh6eiI+Pp6FksgJRUVFIS0tDXPmzMEvv/wCrVaLL774gmNwIqoTLJQuRpZlZGVl4eDBg6KjEJGVqdVqjBs3DgaDAcOGDcOzzz6Lbt26YceOHaKjEZGTY6F0MbGxsfDy8uIqJZETa968ORYuXHitSEZFReGJJ55AcXGx4GRE5KxYKF2Mt7c3Bg0axOODiFzA1U068+fPx/Lly6HVajFnzhyYTCbR0YjIybBQuiC9Xo/du3fj+PHjoqMQkY2p1Wo8/fTTMBgMuP/++zFhwgRERERg+/btoqMRkRNhoXRB9957L9zd3ZGUlCQ6ChHVER8fH3z55ZfYtWsXNBoNoqOjMWrUKBQVFYmORkROgIXSBTVq1AgDBgzgc5RELqhr167YsWMHFixYgNWrV0On02HWrFkcgxORRVgoXZQsy9i2bRsf0idyQSqVCqNHj4bBYMDIkSPx0ksvoUuXLtiyZYvoaETkoFgoXVRCQgIAYPny5YKTEJEoTZs2xbx585CWloYGDRqgT58+GDlyJE6dOiU6GhE5GBZKF9WsWTP07t2bu72JCOHh4di+fTu++eYbrF+/HjqdDtOnT0d1dbXoaETkIFgoXZher8fGjRtRWloqOgoRCaZSqfDYY4/BYDDg8ccfx6uvvoqwsDBs2rRJdDQicgAslC5s6NChMJlMSE5OFh2FiOxE48aNMXv2bKSnp8PHxwf9+/fH/fffjxMnToiORkR2jIXShbVu3Ro9e/bkbm8iuk5YWBi2bt2KRYsWYcuWLQgICMDHH3+Mqqoq0dGIyA6xULo4WZaxZs0aXLlyRXQUIrIzkiThkUceQX5+PsaMGYPJkycjNDQU69evFx2NiOwMC6WLk2UZFRUVSElJER2FiOxUo0aNMHPmTGRkZMDX1xf33HMPhg8fjmPHjomORkR2goXSxXXo0AGdO3fm2JuI/lGnTp2wefNm/PTTT0hNTUVAQAA++OADVFZWio5GRIKxUBJkWcbKlStRUVEhOgoR2TlJkvDggw8iPz8f48aNwzvvvIOQkBBOOYhcHAslQa/X48qVK9iwYYPoKETkIBo0aIBPPvkEmZmZaNeuHQYPHoyhQ4fiyJEjoqMRkQAslITAwEDodDqOvYnolgUFBWHDhg1YsmQJ0tLSEBgYiHfffZcTDyIXw0JJkCQJer0ey5cvh8lkEh2HiByMJEkYMWIE8vLyMGHCBLz//vsIDg7GypUrRUcjojrCQkkAfn+O8vz589iyZYvoKETkoOrXr4+PPvoI+/fvh7+/P+Lj4xEfH49Dhw6JjkZENsZCSQB+f5fvHXfcwbE3EVlMp9Nh7dq1+OWXX5CZmYmgoCC8/fbbMBqNoqMRkY2wUBKA30dWsiwjKSkJZrNZdBwicnBXH6XJzc3FK6+8go8++ghBQUFYtmwZFEURHY+IrIyFkq7R6/U4ffo0du7cKToKETmJevXq4f3330d2djYCAwMxbNgwDB48GAUFBaKjEZEVsVDSNT179oSfnx+WLl0qOgoROZmOHTti1apVWLZsGfLy8hASEoLJkyejrKxMdDQisgIWSrpGpVJh2LBhSExM5EiKiKxOkiQkJCTgwIEDmDRpEqZPn47AwEAsXbqUf+cQOTgWSqpFlmUcOXIEGRkZoqMQkZPy8vLClClTkJOTg7CwMAwfPhwDBw5Efn6+6GhEdJtYKKmW3r17o2nTptztTUQ25+/vj+TkZCQnJ6OwsBCdOnXCpEmTcOXKFdHRiOgWsVBSLe7u7hgyZAifoySiOhMXF4ecnBy88cYb+OyzzxAYGIiff/6ZY3AiB8JCSdfR6/XIy8tDbm6u6ChE5CI0Gg3eeustHDhwAJGRkbj//vsRExODAwcOiI5GRDeBhZKuExMTg/r163OVkojqXPv27ZGUlISUlBQcO3YMYWFheOWVV3D58mXR0YjoBlgo6ToajQZxcXF8jpKIhImNjUV2djamTJmCefPmQafT4aeffuIYnMhOsVDSX5JlGRkZGXwHLxEJ4+npiddffx15eXmIiorCyJEj0bdvX2RnZ4uORkR/wkJJf2nQoEHQaDRISkoSHYWIXFy7du3wyy+/YO3atSgqKkLnzp3x4osv4uLFi6KjEdH/SArnB/Q3hg4diuLiYqSmpoqOQkQEAKiqqsLMmTPx3nvvoX79+vjkk0/w8MMPQ5Ik0dGIXBpXKOlv6fV67NixA6dOnRIdhYgIAODh4YHXXnsNeXl56N27N0aNGoXo6GhkZmaKjkbk0lgo6W/FxcXBzc2NY28isjtt2rTBkiVLsGHDBpw/fx7h4eF4/vnnUVpaKjoakUviyJtuKDY2FtXV1di4caPoKEREf6m6uhqzZ8/GO++8Ay8vL3z88cd49NFHoVJxzYSorvBPG92QLMvYsmULzp07JzoKEdFfcnd3x8svv4z8/HwMGDAATzzxBHr16oX09HTR0YhcBgsl3VBCQgLMZjNWrFghOgoR0Q21atUKP/74IzZv3ozLly8jMjISzz77LM6fPy86GpHT48ib/lHv3r1Rv359rFq1SnQUIqKbUl1djblz5+Ltt9+Gh4cHPvzwQzzxxBMcgxPZCP9k0T+SZRkbNmzgmW9E5DDc3d3xwgsvID8/H4MGDcKYMWPQs2dPpKWliY5G5JRYKOkfybKMqqoqrlASkcPx8/PDokWLsG3bNlRUVKBbt254+umnUVJSIjoakVNhoaR/1LZtW3Tt2pXv9iYih9WrVy/s3bsXs2fPxpIlS6DVajF//nzU1NSIjkbkFFgo6abo9XqkpKSgvLxcdBQiotvi5uaG8ePHw2AwICEhAWPHjkX37t2xa9cu0dGIHB4LJd0UWZZRXl6OtWvXio5CRGSRFi1a4Ouvv0ZqairMZjN69OiBJ598EmfPnhUdjchhsVDSTenYsSM6derEsTcROY2ePXtiz549mDdvHpKSkqDVajF37lyOwYluAwsl3TRZlpGcnIyqqirRUYiIrEKtVmPs2LEwGAwYPnw4nnvuOURGRiI1NVV0NCKHwkJJN02v1+PixYvYtGmT6ChERFbVrFkzLFiwADt37oSbmxvuvvtuPPbYYzhz5ozoaEQOgYWSblpISAjuuusuLF26VHQUIiKb6NatG3bu3Ikvv/wSycnJ0Gq1mD17Nkwmk+hoRHaNhZJumiRJ0Ov1WLZsGZ8xIiKnpVar8dRTT8FgMOChhx7CCy+8gPDwcGzdulV0NCK7xUJJt0SWZZw7dw7btm0THYWIyKZ8fHzwxRdfYM+ePfD29kbv3r3x8MMP4/Tp06KjEdkdFkq6JV27dkWbNm2425uIXEZERARSU1OxcOFCrF27FjqdDjNmzEB1dbXoaER2g4WSbokkSZBlGYmJiTCbzaLjEBHVCZVKhSeeeAIGgwGjRo3CxIkT0aVLF2zevFl0NCK7wEJJt0yv1+PkyZPYs2eP6ChERHWqSZMm+Pzzz7F37140atQIffv2xYMPPoiTJ0+KjkYkFAsl3bK7774bLVq04G5vInJZnTt3xrZt2/Dtt99i06ZNCAgIwCeffMJzesllsVDSLVOr1Rg6dCgSExOhKIroOEREQqhUKjz66KPIz8/HE088gUmTJiEsLAwbN24UHY2ozrFQ0m2RZRmFhYXIysoSHYWISKjGjRvjs88+Q0ZGBpo3b46YmBiMGDECx48fFx2NqM6wUNJt6du3Lxo3bszd3kRE/xMaGootW7bghx9+wLZt2xAQEIAPP/wQlZWVoqMR2RwLJd0WDw8PxMfH8zlKIqI/kCQJI0eORH5+Pp555hm8+eabCA0Nxdq1a0VHI7IpFkq6bXq9Hjk5OcjPzxcdhYjIrjRs2BDTp09HZmYmWrVqhdjYWMiyjKNHj4qORmQTLJR02+655x7Uq1ePY28ior8RHByMTZs2YfHixdi1axcCAwPx/vvvo6KiQnQ0IquSFG7TJQuMGDEChw8f5pmURET/4PLly3jvvfcwc+ZM3HHHHZg9ezYGDx4sOhaRVXCFkiwiyzLS0tI4xiEi+gcNGjTAtGnTkJWVhTvvvBP33nsvEhIScPjwYdHRiCzGQkkWGTx4MDw8PJCUlCQ6ChGRQwgMDMT69evx888/Iz09HUFBQZgyZQqMRqPoaES3jSNvslh8fDxKS0uxbds20VGIiBxKWVkZPvjgA3z66ado06YNPvvsM8THx4uORXTLuEJJFpNlGb/99huKiopERyEicij16tXD1KlTkZ2dDa1WiyFDhiAuLg4HDx4UHY3olrBQksWGDBkClUqFZcuWiY5CROSQtFotUlJSkJiYiP379yM4OBhvvvkmysvLRUcjuikceZNVxMTEQKVSYd26daKjEBE5tPLycnz44YeYNm0aWrZsiZkzZ2Lo0KGQJEl0NKK/xRVKsgq9Xo9ff/0V58+fFx2FiMiheXt747333kNOTg6Cg4MhyzIGDRoEg8EgOhrR32KhJKsYOnQoampqkJycLDoKEZFTuOuuu7By5UqsWLECBoMBISEheP3111FWViY6GtF1OPImq+nVqxd8fHywfPly0VGIiJyK0WjEtGnT8NFHH6F58+aYMWMG9Ho9x+BkN7hCSVYjyzLWrl2Ly5cvi45CRORUvLy88PbbbyMnJwddunTBfffdh3vuuQd5eXmioxEBYKEkK5JlGZWVlUhJSREdhYjIKXXo0AHLly/HqlWrcPjwYXTq1Amvvvoqv5En4TjyJquKiIjAXXfdhSVLloiOQkTk1CoqKvDpp59i6tSpaNKkCaZPn47777+fY3ASgiuUZFV6vR6rVq1CRUWF6ChERE5No9HgjTfeQG5uLrp3744HH3wQ/fv3R05Ojuho5IJYKMmqZFlGWVkZz6MkIqojd9xxBxITE7FmzRqcOHECnTt3xssvv4xLly6JjkYuhIWSrCogIABBQUFITEwUHYWIyKUMHDgQ+/fvx3vvvYf58+dDp9Phxx9/BJ9so7rAQklWJ8syVqxYgerqatFRiIhciqenJyZNmoS8vDxER0fj4YcfRp8+fbB//37R0cjJsVCS1en1ely4cAGbN28WHYWIyCW1bdsWP//8M9avX4/i4mJ06dIFEyZMQGlpqeho5KRYKMnqwsLC0L59eyxdulR0FCIilxYTE4PMzEx8+OGHWLhwIXQ6Hb777juYzWbR0cjJsFCS1UmSBL1ej2XLlqGmpkZ0HCIil+bh4YGJEyciPz8f/fr1w2OPPYbo6Gjs27dPdDRyIiyUZBOyLOPMmTNITU0VHYWIiAC0bt0aixcvxqZNm1BaWoqIiAiMHz8eFy5cEB2NnAALJdlE9+7d0apVK+72JiKyM3379sW+ffvwySefYNGiRdDpdPj66685BieLsFCSTahUKgwbNgyJiYk8soKIyM64u7vjpZdeQn5+PgYOHIgnn3wSUVFR2Lt3r+ho5KBYKMlm9Ho9jh07xr+giIjsVMuWLfH9999j69atKC8vR9euXTF27FicP39edDRyMCyUZDPR0dHw8fHhbm8iIjsXHR2N9PR0zJo1Cz/99BO0Wi2++uorbqykm8ZCSTbj5uaGoUOHYunSpRx7ExHZOTc3Nzz//PMwGAyIi4vD008/jR49emD37t2io5EDYKEkm5JlGQUFBcjJyREdhYiIboKvry++/fZbbN++HSaTCT169MCYMWNw7tw50dHIjrFQkk31798fDRs25G5vIiIHc/fddyMtLQ1z5szBL7/8Aq1Wiy+++IJjcPpLLJRkU56enoiLi+NzlEREDkitVmPcuHEwGAwYNmwYnn32WXTr1g07duwQHY3sDAsl2Zwsy8jKysLBgwdFRyEiotvQvHlzLFy48FqRjIqKwhNPPIHi4mLBychesFCSzcXGxsLLywtJSUmioxARkQWubtKZP38+li1bBp1Oh88//xwmk0l0NBKMhZJsrl69eoiNjeXYm4jICajVajz99NMwGAwYMWIEnn/+eURGRmL79u2io5FALJRUJ/R6PXbt2oUTJ06IjkJERFbQrFkzfPnll9i1axc8PT0RHR2NUaNGoaioSHQ0EoCFkurEvffeC3d3d469iYicTNeuXbFjxw4sWLAAq1evhk6nw6xZszgGdzGSwhOnqY4MHjwYRqMRv/76q+goRERkA+fPn8cbb7yB+fPnIzg4GHPnzsX//d//iY5FdYArlFRnZFnG1q1bcfbsWdFRiIjIBpo2bYp58+YhLS0N9evXR+/evTFy5EicOnVKdDSyMRZKqjMJCQkAgOXLlwtOQkREthQeHo7ffvsNX3/9NdavXw+dTofp06ejurpadDSyEY68qU717dsXGo0GKSkpoqMQEVEdKC0txVtvvYW5c+deO2aoX79+omORlXGFkuqUXq/Hxo0bUVpaKjoKERHVgcaNG2P27NlIT0+Hj48P+vfvj/vvv5+nfjgZFkqqU8OGDUN1dTVWrlwpOgoREdWhsLAwbN26FYsWLcKWLVsQEBCAjz/+GFVVVaKjkRVw5E11rmfPnmjZsiUSExNFRyEiIgEuXryId955B3PmzMFdd92FOXPmYMCAAaJjkQW4Qkl1TpZlrFmzBmVlZaKjEBGRAI0aNcLMmTORkZEBX19f3HPPPRg+fDiOHTsmOhrdJhZKqnOyLMNoNGLNmjWioxARkUCdOnXC5s2b8eOPPyI1NRWBgYGYOnUqKisrRUejW8RCSXXO398fYWFhfLc3ERFBkiQ89NBDyMvLw9ixY/H222+jU6dOXHRwMCyUJIRer8fKlSv5XSgREQEAGjZsiE8//RSZmZlo06YNBg0ahKFDh+LIkSOio9FNYKEkIWRZxuXLl7FhwwbRUYiIyI4EBQVh48aNWLJkCdLS0hAYGIh3330XFRUVoqPRDbBQkhBBQUHQ6XTc6U1ERNeRJAkjRoxAXl4eJkyYgPfffx/BwcE8cs6OsVCSEJIkQZZlLF++HCaTSXQcIiKyQ/Xr18dHH32E/fv3w9/fH/Hx8YiPj8ehQ4eE5lIUBeXmclyquYQLNRdwqeYSys3lcOWTGHkOJQmzd+9eREZGYuPGjXwNFxER3ZCiKEhMTMSLL76I4uJivPbaa5g0aRK8vLxsfm+j2YjjpuMoNhWjqKYIxaZiVOP695K7wx0t3FrAT+2HFm4t0NatLbxUts9nD1goSRhFUXDnnXciLi4Oc+fOFR2HiIgcQFlZGaZOnYpPP/0UrVq1wqxZszBkyBBIkmTV+yiKgqKaImRWZKKgugBmmKGCCmaY//GzV79OBRW07lqEakLhp/azekZ7wkJJQr344otYsmQJTpw4AZWKT2AQEdHNKSgowPPPP481a9Zg0KBB+Oyzz9CxY0erXLuwqhA7jDtQYi6BBAkKbr8qXf28j8oHUV5R6ODRwSoZ7Q3/BSeh9Ho9Tp8+jZ07d4qOQkREDqRjx45YvXo1li1bhtzcXISEhGDy5MkWvYXNaDYi5UoKVpatRIm5BAAsKpN//HyJuQTJZclIuZICo9lo0TXtEVcoSaiamhq0bt0aDz/8MD799FPRcYiIyAEZjUZ89NFH+Pjjj+Hr64sZM2ZAluVbGjEXVhViQ/kGVCqVFpfIG5EgwVPyRIx3DPw9/G12n7rGFUoSSq1WY9iwYVi6dKlL744jIqLb5+XlhSlTpiAnJwehoaEYPnw4Bg4ciPz8/H/8rKIo2G3cjZVlK1GhVNi0TAK/r1hWKBVYWbYSe4x7nObfPhZKEk6WZRw5cgT79u0THYWIiByYv78/kpOTkZycjMLCQnTq1AmTJk3ClStX/vLrFUVBakUqdlTsqOOkv0utSEVqRapTlEoWShKuT58+aNKkCQ85JyIiq4iLi0NOTg7eeOMNfPbZZwgMDMTPP/98XXHbU7EHaRVpglL+Lq0iTXgGa2ChJOHc3d0xZMgQLF26VHQUIiJyEhqNBm+99RYOHDiAiIgI3H///YiJiUFubi6A/+3kFrQy+WepFakorCoUHcMiLJRkF/R6PXJzc6/9QSciIrKG9u3bY9myZVi9ejWOHTuG0NBQvPbWa9hQtkF0tFo2lG9w6N3fLJRkFwYMGID69etz7E1ERDYxaNAgZGdnY8qUKTh/13lUKBWiI9VSqVRiS/kW0TFuG48NIrvxwAMPoKCgAHv37hUdhYiInFRhVSFWlq0UHeNvxdeLd8jDz7lCSXZDlmWkp6fj8OHDoqMQEZETUhQFO4z28dzk30k1OuaubxZKshuDBw+Gp6cnkpKSREchIiInVFRTdO0NOPaqxFyCopoi0TFuGQsl2Y369etj4MCB3O1NREQ2kVmRCQk3//acv5PyUQpeaPoCrpT89fmWlpAgIasiy+rXtTUWSrIrer0eqampOH36tOgoRETkRIxmIwqqC2z+JhxLKVBgqDY43I5vFkqyK/Hx8XBzc+PYm4iIrOq46TjMMIuOcVPMMOOE6YToGLeEhZLsSpMmTdCvXz8eH0RERFZVbCqGykFqjwoqnDGdER3jlriJDkD0Z7IsY9y4cSgpKYGPj4/oOERE5ASKaoqsvkJZVlKGX175Bbkbc6F2VyPyvkjEvxMPd427Rdc1w4wzNY5VKB2jqpNLGTp0KMxmM1asWCE6ChEROQFFUVBsKrb6db994ltUV1Yj7q04BA0IwtavtmLJi0uscu1iU7FDHR/EQkl2x9fXF7169eJubyIisgqjYkQ1qq1+XZ87fDDmpzGIHh2Nh+c/jF5P9kLakjScyjll8bWrUAWj4jgbc1goyS7p9XqsX78ely5dEh2FiIgcnEkx2eS6vZ7sVevH0WOiAQAH1h+wyvVtldsWWCjJLg0bNgxVVVVYtWqV6ChEROTgalBjk+s2929e68fN2jeDpJJw/th5q1zfVrltgYWS7FK7du3QtWtX7vYmIqKbZjL99YqeGuq6CWD5mem11FluK2ChJLslyzJWr16N8vJy0VGIiMjOHThwABqNBkFBQXj55ZexZs2aa/9+uEm2OdTmbOHZWj8+d+gcFLOCpu2aWuX6tsptCyyUZLdkWUZ5eTnWrVsnOgoREdm5Bg0aoKamBrm5uZg9ezYGDRqERo0aoVOnTnj+qefhDsuO8vkr2xdur/XjbQu2AQACYwItvrYHPOAleVl8nbrCQkl2S6vVIiQkhLu9iYjoH3l4eKB+/foA/v/o22QyITs7G99++y2aSc2sfs+SoyVY8NACbF+4HT888wO2L9yOiOERaB3S2uJrt3BrAUmy8gzdhlgoya7Jsozk5GRUVVWJjkJERHbi3LlzWLt2LT744APIsox27drBz88PV65cqfV1kiQhMDAQBQUFaOXRyupvynl04aNw83BD8pRkHFh3ANFjovHA7Acsvq4KKviqfa2QsO5IiiOdmkkuJysrC2FhYUhJSUFsbKzoOEREVMdKSkqwd+9e7N27F2lpadi7dy+OHj0KAGjcuDEiIiIQERGByMhIbN26FfPnz7+2Qjl69GjMmTMHGo0GhioDUspSRP5WbsngeoPR0aOj6Bg3zXGe9iSX1KlTJ/j7+yMxMZGFkojIyV24cKFWcUxLS8ORI0cAAI0aNUJ4eDhGjBhxrUB26NCh1ljY09MTn3/+Odzd3fHFF1/gySefvPZrbd3aQgWV1V+/aAsqqNDGrY3oGLeEK5Rk91577TV88803OH36NNRqxzlCgYiI/l5paSnS09NrlcdDhw4B+H2DzR9XHiMiIuDv7w+V6sYj60uXLmHChAkYP348IiIirvv1NVfWwFBtgAL7rT4SJOjcdRhYf6DoKLeEhZLs3q5du9CjRw9s3rwZvXv3Fh2HiIhu0cWLF5Genl5r9fHgwYMAgPr16yM8PLxWeezYseM/lsfbcdp0Gj9f/tnq17W2EQ1GoKVbS9ExbglH3mT3unbtijZt2iAxMZGFkojIzl26dAkZGRm1Vh4LCgoAAPXq1UOXLl0QFxd3rUBqtVqblMe/4qf2g4/KByXmkjq53+1opmoGP7Wf6Bi3jCuU5BCef/55JCUl4ejRo3X2Fw8REd3YlStXriuPBoMBiqLA29sbnTt3RmRk5LWVR51OJ/zRpUNVh5Bcliw0w43E14tHB48OomPcMhZKcghbtmxBnz59sGvXLnTr1k10HCIil1NWVoaMjIxaY+u8vDwoigKNRoMuXbrUGlsHBATAzc0+B6EpV1JQUF1gV89SSpCgddcitr5jbkBloSSHUFNTg5YtW+Lxxx/Hxx9/LDoOEZFTKy8vx759+2qVx9zcXJjNZmg0GoSFhV0rjpGRkQgMDLTb8vhXjGYjFl1ahAqlQnSUazSSBqMajoKXynHejvNHLJTkMJ566in8+uuvMBgMDvX2ACIie2Y0GpGZmVlrbH3gwAGYzWZ4eHhcVx6DgoLg7m791xjWtcKqQqwsWyk6xjVx9eLg7+EvOsZtY6Ekh7FmzRoMGjQImZmZCA0NFR2HiMjhVFRUICsrC2lpadcKZE5ODmpqauDh4YHQ0NBaY+vg4GB4eHiIjm0zu427saNih+gYiNJEoatXV9ExLOI469Pk8vr164dGjRohMTGRhZKI6B9UVlYiKyur1tg6OzsbJpMJ7u7u6NSpE3r06IFx48YhMjISISEhTl0e/0pXTVdUoxppFWnCMkRqIhGpiRR2f2vhCiU5lEceeQT79u3D/v37RUchIrIbVVVV2L9/f62xdXZ2Nqqrq+Hm5oaQkJBaY+tOnTrB09NTdGy7oCgK0irSkFqRWuf3jvKKQleNY69MXsVCSQ5l2bJlGDZsGPLz86HVakXHISKqc1VVVcjJyalVHrOyslBdXQ21Wo2QkJBaY+vQ0FBoNBrRse1eYVUhNpRvQKVSadPd3xIkeEqeiPGOcehnJv+MhZIcSnl5OZo3b44333wTkyZNEh2HiMimqqurkZOTU2tsnZmZiaqqKqhUKgQHB9daeQwNDYWXl2PuErYHRrMRm8s3w1BtsNk9dO469PHuA43KuUo+CyU5nPvuuw9Hjx7F7t27RUchIrIak8mEAwcO1Fp5zMzMRGVlJVQqFQIDA2uVx7CwMHh7e4uO7ZQKqwqxw7gDJeYSSJAsWrG8+nkflQ+ivKIc8tDym8FCSQ5n8eLFeOihh3D06FG0a9dOdBwioltmMpmQl5dXqzzu27cPFRUVkCQJgYGBtcbWnTt3Rr169UTHdimKoqCopghZFVkwVBtghhkqqGCG+R8/e/XrVFBB665FmCYMvmpfpz7yjoWSHM6lS5fQvHlzTJs2DRMmTBAdh4johmpqapCXl1drbJ2RkQGj0QhJkqDT6WqVxy5duqB+/fqiY9MfGM1GnDCdwBnTGZypOYMzpjOoRvV1X+cOd/i6+cJX7QtfN1+0cWvjsAeV3yoWSnJIcXFxuHTpErZu3So6ChHRNTU1NTAYDLVWHjMyMlBeXg4A0Gq1tcbWXbp0QYMGDQSnplulKAqMihEmxYQa1EANNdwkN3hJXk69CnkjLJTkkL755hs8+eSTOH36NHx9fUXHISIXZDabUVBQcF15vHLlCgCgY8eO1608NmrUSHBqIttgoSSHVFJSAl9fX8ydOxdPP/206DhE5OTMZjMOHjxYa2ydnp6Oy5cvAwD8/f1rlcfw8HA0btxYbGiiOsRCSQ4rJiYGarUaa9euFR2FiJyIoigoLCy8Vh7T0tKQnp6OS5cuAQDat29fa2wdHh6OJk2aCE5NJBYLJTmsefPmYcKECSguLuZf5kR0WxRFweHDh2uNrdPT01FaWgoAuOOOOxAZGVlr5dHHx0dsaCI7xEJJDuv06dNo3bo1vv32W4waNUp0HCKyc4qi4MiRI7XG1nv37sWFCxcAAO3atas1to6IiECzZs0EpyZyDCyU5NDuvvtuNGvWDMuXLxcdhYjsiKIoOHbsWK3ymJaWhvPnzwMA2rRpU2tsHRERgebNmwtOTeS4WCjJoU2fPh2TJ0/GuXPneG4bkYtSFAUnTpyoVRz37t2Lc+fOAQBatWp1XXnk6RBE1sVCSQ7t8OHD6NChA5YsWYIRI0aIjkNENqYoCk6ePHndyuPZs2cBAH5+frWeeYyIiEDLli0FpyZyfiyU5PDCw8Oh1Wrxn//8R3QUIrKyU6dOXVcez5w5AwDw9fW9buWxVatWghMTuSY30QGILKXX6/HRRx+hoqICGo1GdBwiuk1FRUXXja1Pnz4NAGjevDkiIyMxZsyYa+WxdevWLvtWEiJ7wxVKcni5ubkICgrCihUrEB8fLzoOEd2EM2fOXLfyeOrUKQBAs2bNaq06RkZGok2bNiyPRHaMhZKcQlBQELp3745vvvlGdBQi+pOzZ89eVx5PnDgBAGjatOm10ni1QLZr147lkcjBcORNTkGWZcybNw/V1dVwd3cXHYfIZZ07d+7a+Y5XC+SxY8cAAI0bN0ZkZCRGjhx5rTzeeeedLI9EToArlOQUMjIyEB4ejvXr1yMmJkZ0HCKXcP78+VrlMS0tDUePHgUANGrU6Lqxdfv27VkeiZwUCyU5BUVR0KFDB8TGxuKLL74QHYfI6Vy4cAHp6em1xtaHDx8GADRs2BDh4eG1xtYdOnSASqUSnJqI6goLJTmNV155BT/88ANOnjwJtVotOg6RwyotLUV6enqtsXVhYSEAoH79+tfOd7xaHu+66y6WRyIXx0JJTiM1NRV33303tm3bhl69eomOQ+QQLl26dF15LCgoAADUq1fv2srj1QLZsWNHlkciug4LJTkNs9mMNm3a4IEHHsCMGTNExyGyO5cvX0ZGRkatsbXBYAAAeHt7o0uXLrXKo1ar5Wo/Ed0UFkpyKuPGjcOqVatw+PBhPvxPLu3KlSvIyMiotfKYn58PRVHg5eWFLl261BpbBwQEsDwS0W1joSSnsmnTJvTv3x9paWmIiIgQHYeoTpSVlWHfvn21ymNubi4URYFGo0Hnzp1rrTwGBATAzY2nxhGR9bBQklMxmUzw8/PDU089halTp4qOQ2R15eXlyMzMrDW2zs3NhdlshqenJ8LCwmqVx6CgIJZHIrI5FkpyOk8++SS2b9+OvLw8jr3JoRmNRmRmZtZaeTxw4ABqamrg4eGBsLCwWmPr4OBgHuxPREKwUJLTWbVqFeLi4pCdnY3g4GDRcYhuSkVFBbKysmqVx+zsbNTU1MDd3R2hoaG1XlEYHBwMDw8P0bGJiADw1YvkhGJiYtCgQQMkJiayUJJdqqysxP79+2uNrbOzs2EymeDm5oZOnTqhW7duGDt2LCIjIxESEgJPT0/RsYmI/hZXKMkpPfTQQzhw4AD27dsnOgq5uKqqKmRnZ9cqj/v370d1dTXUajVCQkJqvWGmU6dO0Gg0omMTEd0SFkpySkuXLsXw4cNx8OBB+Pv7i45DLqK6uhrZ2dm1xtZZWVmoqqqCWq1GcHBwrWceQ0ND4eXlJTo2EZHFWCjJKZWVlaF58+aYMmUKJk6cKDoOOaHq6mocOHCg1spjVlYWKisroVKpEBQUVGu3dVhYGMsjETktFkpyWrIs4/Tp09ixY4foKOTgTCYTcnNza5XHzMxMVFRUQKVSISAg4LryWK9ePdGxiYjqDAslOa0ffvgBjzzyCI4fP442bdqIjkMOwmQyIS8vr9bYet++fTAajZAkCQEBAbXG1p07d0b9+vVFxyYiEoqFkpxWaWkpWrRogRkzZmD8+PGi45AdqqmpQX5+fq2Vx3379qG8vBwAoNPpapXHLl26oEGDBoJTExHZHxZKcmqDBg1CRUUFfv31V9FRSDCz2QyDwVCrPGZkZKCsrAwA0LFjx1pj6y5duqBhw4aCUxMROQYWSnJqCxYswDPPPIOioiI0b95cdByqI2azGQUFBbXG1unp6bhy5QoAwN/fv9ZRPeHh4WjUqJHg1EREjouFkpxacXExWrZsiS+//BKjR48WHYdswGw2o7CwsNbKY3p6Oi5fvgwA6NChQ62xdXh4OJo0aSI4NRGRc2GhJKfXp08feHt7Y/Xq1dd+TlEUGBUjTIoJNaiBGmq4SW7wkrz4/m87pigKDh06dF15vHjxIgDgzjvvrDW2Dg8PR9OmTQWnJiJyfiyU5PTmzJmDNz94E5vzNuOKxxUU1RSh2FSMalRf97XucEcLtxbwU/uhhVsLtHVrCy8Vzw4UQVEUHD58uNbYeu/evSgtLQUAtGvXrlZ5jIiIgI+Pj9jQREQuioWSnJaiKCiqKcLOCztxWDkMtbsaKqhghvkfP3v161RQQeuuRagmFH5qP65e2oiiKDh69Gitlce9e/fiwoULAIC2bdvWKo4RERF8JpaIyI6wUJJTKqwqxA7jDpSYSyBBgoLb/9/86ud9VD6I8opCB48OVkzqehRFwfHjx68rjyUlJQCA1q1bXyuPVwtkixYtBKcmIqIbYaEkp2I0G7G5fDMM1Qab3UPrrkUf7z4chd8ERVFw4sSJWsUxLS0N586dAwC0bNnyurG1n5+f4NRERHSrWCjJaRRWFWJD+QZUKpUWrUj+EwkSPCVPxHjHwN/D32b3cTSKouDUqVPXSuPVAllcXAwA8PX1rbXqGBERgVatWglOTURE1sBCSQ5PURTsqdiDHRV1/87uKE0UIjWRLvls5enTp68bWxcVFQEAmjdvXqs8RkZGolWrVi7534mIyBWwUJJDUxQFqRWpSKtIE5YhUhOJKE2UU5eloqKi68bWp0+fBgA0a9bsurF1mzZtnPq/BxER1cZCSQ5tt3G3kJXJP4vSRKGrV1fRMayiuLj4urH1yZMnAQBNmza9rjy2a9eO5ZGIyMWxUJLDKqwqxMqylaJjXBNXL87hnqk8e/bstfMdr5bH48ePAwCaNGlSqzhGRkbijjvuYHkkIqLrsFCSQzKajVh0aREqlArRUa7RSBqMajjKbnd/l5SUXDe2PnbsGACgUaNG15XH9u3bszwSEdFNYaEkh5RyJQUF1QU23c19qyRI0LprEVs/VnQUnD9/Hunp6bXG1keOHAEANGzY8Nou66sF0t/fn+WRiIhuGwslORx7G3X/WXy9+Do9/PzChQtIT0+vtfp46NAhAED9+vVrlcfIyEj4+/tDpVLVWT4iInJ+bqIDEN0KRVGwwyh+E86NpBpT0d7dNuPiixcvXlt5vFogCwsLAfxeHrt06YKEhIRrBbJjx44sj0REZHMslORQimqKUGIuER3jhkrMJSiqKUJLt5YWXefSpUvIyMioVR4LCgoAAN7e3ggPD0dcXNy1sbVWq4VarbbGb4GIiOiWcORNDmXNlTUwVBvs6tnJP5MgQeeuw8D6A2/6M5cvX0ZGRkatsXV+fj4AwMvLC126dKn1zGNAQADLIxER2Q0WSnIYRrMR/774b5hhFh3lH6mgwuhGo/9yx/eVK1ewb9++WiuP+fn5UBQFGo0GnTt3rrXbOiAgAG5uHCYQEZH94r9S5DCOm447RJkEADPMOGE6gdam1teVx9zcXCiKAk9PT4SFhaFfv36YOHEiIiMjERQUxPJIREQOhyuU5DC2l29HRmWGY5RKM5C5OBPfTfgOZrMZHh4eCAsLqzW2Dg4Ohru7u+ikREREFmOhJIfxy+VfcNJ00uLrnD9+Hhs/2wjDVgNKT5TC3csdHaM7Ysi7Q+DTzscKSQEoQNmhMjTPaI7IyEgEBwfDw8PDOtcmIiKyM5ytkUNQFAXFpmKrXOtY+jEc3n0Y4cPC0bhVY5w/fh6/ff0bPo//HP/a8S94eFuh+ElAE/8mGB0xmgeGExGR02OhJIdgVIyoRrVVrhV0TxA6J3Su9XPBA4Mxa+AsZCZnouv9Xa1ynypUwagY4S15W+V6RERE9oonHpNDMCkmq13Lw+v/r0DWVNeg7HwZmnVoBq9GXjiRecJq9wGsm5uIiMhecYWSHEINaqx2rSpjFTbM3IDdP+3GxdMX8cfHiI2XjFa7D2Dd3ERERPaKhZIcghrWO8Q78bVE7PppF3o/0xt3dr0TXg29AAlYNHoRrL1HzZq5iYiI7BULJTkEN8l6/6vuW7EPXR/oiqHvD732c9UV1TBetO7qJGDd3ERERPaKz1CSQ/CSvOAO65zZqFKr8Oc3N277ahvMNdY939IDHvCSrn9TDhERkbPh8gk5BEmS0MKthVXOoQweGIy0n9OgaaiBn84PR/YcgWGLAfWa1rNC0v+vhVsLHhlEREQugYWSHIaf2g+nTactflPOsA+HQVJJ2PvLXpgqTWjfrT3GJo3F/OHzrZT093d5+6p9rXY9IiIie8Y35ZDDMFQZkFKWIjrGTRtcbzA6enQUHYOIiMjm+AwlOYy2bm2hcpD/ZVVQoY1bG9ExiIiI6oRj/OtMBMBL5YWO7h0hwb6fS5QgQeuuhZeKG3KIiMg1sFCSQwnThEH58xZtO6NAQagmVHQMIiKiOsNCSQ7FT+0HH5WP6Bg31EzVDH5qP9ExiIiI6gwLJTkUSZIQ5RUlOsYN9fTqyeOCiIjIpbBQksPp4NEBWnet3T1LKUGCzl2HDh4dREchIiKqUyyU5JD6ePeBp+QpOkYtnpInenv3Fh2DiIiozrFQkkPyUnkhxjtGdIxaYrxjuLObiIhcEgslOSx/D3/01PQUHQMAEKWJgr+Hv+gYREREQrBQkkPrqumKSE2k0AyRmkjhGYiIiETiqxfJ4SmKgrSKNKRWpNb5vaO8otBV07XO70tERGRPWCjJaRRWFWJD+QZUKpU2PfxcggRPyRMx3jEccxMREYGFkpyM0WzE5vLNMFQbbHYPnbsOfbz7QKPS2OweREREjoSFkpxSYVUhdhh3oMRcAgmSRSuWVz/vo/JBlFcUz5kkIiL6ExZKclqKoqCopghZFVkwVBtghhkqqGCG+R8/e/XrVFBB665FmCYMvmpfvgGHiIjoL7BQkkswmo04YTqBM6YzOFNzBmdMZ1CN6uu+zh3u8HXzha/aF75uvmjj1oZnSxIREf0DFkpySYqiwKgYYVJMqEEN1FDDTXKDl+TFVUgiIqJbxEJJRERERBbhweZEREREZBEWSiIiIiKyCAslEREREVmEhZKIiIiILMJCSUREREQWYaEkIiIiIouwUBIRERGRRVgoiYiIiMgiLJREREREZBEWSiIiIiKyCAslEREREVmEhZKIiIiILMJCSUREREQWYaEkIiIiIouwUBIRERGRRVgoiYiIiMgiLJREREREZBEWSiIiIiKyCAslEREREVmEhZKIiIiILMJCSUREREQWYaEkIiIiIouwUBIRERGRRVgoiYiIiMgiLJREREREZBEWSiIiIiKyCAslEREREVnk/wGyIMDQMBTKxQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "({'a': [], 'b': ['a'], 'c': ['a', 'b']}, ['a', 'b', 'c'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_graph(graph_gt_tabular)\n",
    "graph_gt_tabular, var_names_tabular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c934b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "({'a': [], 'b': [('a', -1)], 'c': [('a', -2), ('b', -2)]}, ['a', 'b', 'c'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_graph(graph_gt_time_series)\n",
    "graph_gt_time_series, var_names_time_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00ad7fc",
   "metadata": {},
   "source": [
    "## Data Generation with intervention\n",
    "\n",
    "It is also possible to generate data with interventions. For instance, below is the code that generates data without any intervention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "546fbab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn = lambda x:x\n",
    "F1=F2=F3 = fn\n",
    "k = 0.1\n",
    "k1=k2=k3=k\n",
    "sem_tabular = {\n",
    "        'a': [], \n",
    "        'b': [('a', k1, F1)], \n",
    "        'c': [('a', k2, F2), ('b', k3, F3)],\n",
    "        }\n",
    "T = 5000 # number of samples in case of tabular data, and length of time series in case of time series data\n",
    "data_tabular, var_names_tabular, graph_gt_tabular = DataGenerator(sem_tabular, T=T, seed=0, discrete=False)\n",
    "\n",
    "plt.plot(data_tabular[-100:,1], label='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89234640",
   "metadata": {},
   "source": [
    "Now, if we want to generate data corresponding to the same data generating process as above, except, we now want to intervene a specific variable, it can be done by specifying the argument intervention as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80d62e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "t='b' \n",
    "\n",
    "intervention = 2*np.ones(T)\n",
    "intervention_data,_,_ = DataGenerator(sem_tabular, T=T, seed=0, intervention={t:intervention})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ee35cf",
   "metadata": {},
   "source": [
    "Note how the value of the argument seed is the same in both the data generation with and without intervention. This is important to ensure that the data is generated via the same process in both case, with the only difference being the intervention in the latter case.\n",
    "\n",
    "We verify that the variable has been intervened and set to the specified value by plotting it below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b7e7513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(intervention_data[-100:,1], label='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d63804d",
   "metadata": {},
   "source": [
    "This kind of intervention data can be used to calculate ground truth values in the case of causal inference estimation. Such examples can be found in the tutorials on causal inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c46bb3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
